Retrieval Robust to Object Motion Blur
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Author / Producer
Date
2025
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Moving objects are frequently seen in daily life and usually appear blurred in images due to their motion. While general object retrieval is a widely explored area in computer vision, it primarily focuses on sharp and static objects, and retrieval of motion-blurred objects in large image collections remains unexplored. We propose a method for object retrieval in images that are affected by motion blur. The proposed method learns a robust representation capable of matching blurred objects to their deblurred versions and vice versa. To evaluate our approach, we present the first large-scale datasets for blurred object retrieval, featuring images with objects exhibiting varying degrees of blur in various poses and scales. We conducted extensive experiments, showing that our method outperforms state-of-the-art retrieval methods on the new blur-retrieval datasets, which validates the effectiveness of the proposed approach. Code, data, and model are available at https://github.com/Rong-Zou/Retrieval-Robust-to-Object-Motion-Blur.
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Publication status
published
External links
Book title
Computer Vision – ECCV 2024
Journal / series
Volume
15142
Pages / Article No.
251 - 268
Publisher
Springer
Event
18th European Conference on Computer Vision (ECCV 2024)
Edition / version
Methods
Software
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Date collected
Date created
Subject
object retrieval; object motion blur; datasets
Organisational unit
03766 - Pollefeys, Marc / Pollefeys, Marc